The present invention generally relates to biological tissue imaging, and more particularly to a computer-based system and method for analyzing the texture of tissue samples and classifying the tissue to detect abnormalities which provides a medical diagnostic tool.
Breast cancer is a high incidence cancer in women worldwide. The survival rate of breast cancer improves with screening and early detection. Biopsy is a frequently used medical test in which tissue samples are removed from a human subject and then examined by a pathologist under a microscope to determine the presence or extent of a disease. Traditionally, the tissue is processed to extremely thin slices and stained before being observed under a microscope. Optical coherence tomography (OCT) provides an alternative non-invasive optical imaging modality that can provide 3D, high-resolution images of biopsy sample tissues without staining. Optical coherence microscopy (OCM) combines the advantages of OCT and confocal microscopy using high numerical aperture objectives to provide cellular resolution images. OCT, OCM and other images captured by optical imaging devices are label free, in the sense that they do not use staining of samples to indicate different tissue areas. In contrast, traditional histological images rely on staining of samples with at least one biomarker. Thus, in order to classify different tissue types in OCT, OCM and other optical images, advanced computer algorithms are required to analyze the appearance differences in different tissue areas.
To improve accuracy and efficiency, numerous computer aided diagnostic methods implementing software program algorithms for automated classification of tissue areas in medical images have been proposed. The same level of accuracy offered by such automated techniques and speed cannot reasonably be duplicated by manual analysis of tissue images alone. Experiments show that a spectral texture analysis technique is good at extracting distinctive texture features from OCT images, even if the images have few structure features. Image processing and data mining techniques are applied on a large number of medical images to distinguish different patterns of tissues.
Texture analysis is a commonly used technique for medical image classification. There are mainly two different kinds of texture analysis techniques: structure based methods and statistical methods, including spectrum analysis and feature distributions.
In the structure based methods, typical texture patterns of an image are extracted and clustered into several groups. The frequency of the occurrence of each pattern is used to represent the texture. Structure based methods are good at classification of texture images that are highly repetitive, since the clustering step reserves more information from the texture image if a certain kind of texture pattern appears in the image frequently. But for OCT/OCM images of human tissue, very few texture patterns are exactly the same, so that structure based methods do not perform well in OCT/OCM image based tissue classification.
In statistical methods, required features are extracted and the frequency of the occurrence of each feature in the whole texture image is used to represent the texture. Different texture features are used in statistical methods, including grey-level co-occurrence matrix, center-symmetric auto-correlation, gray level difference, and local binary patterns (LBP). The statistical distribution of different features are calculated and used as feature vectors for texture representation. Since the statistical methods do not require texture images to be highly repetitive, they are suitable to be used for the OCT/OCM image based tissue classification.
LBP is a popular statistical texture analysis technique pursued in recent years. In LBP features, the frequency of different local image intensity feature patterns is calculated and used as feature vectors. LBP features have been applied in many applications, such as texture analysis, face recognition, and description of regions of interest. LBP is rotation invariant and uses a fixed set of patterns to represent the texture. It has high classification accuracy on different texture image datasets. Although LBP is relatively effective in representing and classifying textures in OCT/OCM images, further improvement in tissue classification accuracy however are desired particularly for medical diagnostic purposes.